1. Field of the Invention
The present invention relates generally to endpoint detection during semiconductor manufacturing.
2. Discussion of the Background
The inventors have identified problems with conventional processing reactors and methods of using those reactors that are solved by the present invention.
Typically, during semiconductor processing, a (dry) plasma etch process is utilized to remove or etch material along fine lines or within vias or contacts patterned on a silicon substrate. The plasma etch process generally involves positioning a semiconductor substrate with an overlying patterned, protective layer, for example a photoresist layer, into a processing chamber. Once the substrate is positioned within the chamber, an ionizable, dissociative gas mixture is introduced within the chamber at a pre-specified flow rate, while a vacuum pump is throttled to achieve an ambient process pressure. Thereafter, a plasma is formed when a fraction of the gas species present are ionized by electrons heated via the transfer of radio frequency (RF) power either inductively or capacitively, or microwave power using, for example, electron cyclotron resonance (ECR). Moreover, the heated electrons serve to dissociate some species of the ambient gas species and create reactant specie(s) suitable for the exposed surface etch chemistry. Once the plasma is formed, any exposed surfaces of the substrate are etched by the plasma. The process is adjusted to achieve optimal conditions, including an appropriate concentration of desirable reactant and ion populations to etch various features (e.g., trenches, vias, contacts, etc.) in the exposed regions of substrate. Such substrate materials where etching is required include silicon dioxide (SiO2), poly-silicon and silicon nitride.
As the feature size shrinks and the number and complexity of the etch process steps used during integrated circuit (IC) fabrication escalate, the requirements for tight process control become more stringent. Consequently, real time monitoring and control of such processes becomes increasingly important in the manufacture of semiconductor ICs. For example, one such monitoring and control diagnostic necessary for the timely completion of an etch step or process is endpoint detection.
Endpoint detection refers to the control of an etch step and, in particular, to the detection of the feature etch completion or the exact instant in time when the etch front reaches the etch stop layer. If the etch process endpoint is improperly detected, then severe under-cutting of the feature may occur due to over-etching or partially complete features may result due to underetching. As a result, poor endpoint detection could lead to devices of poor quality that are subject to increased risk of failure. Therefore, the accurate and precise completion of an etch process is an important area for concern during the manufacturing process.
One approach used for endpoint detection is to monitor the emission intensity of light at a pre-specified wavelength in time using optical emission spectroscopy (OES). Such a method might identify a wavelength corresponding to a chemical species present in the etch process that shows a pronounced transition at the etch process endpoint. Subsequently, a resultant signal is analyzed to detect distinct variations in the emission intensity which, and the analysis of the resulting signal is then used to correlate with the completion of an etch process. Typically, the species selected corresponds to a reactive species or a volatile etch product. For example, the selected wavelength may correspond to CO* emission when etching SiO2 and polymer films, N2* or CN* emission when etching nitride films, SiF* emission when etching poly-silicon and AlCl* emission when etching aluminum.
In addition to the approach of monitoring the emission intensity at a single wavelength as described above, another approach is to monitor the light intensity at two wavelengths and record the ratio (or some mathematical manipulation thereof) of the two intensities. For instance, one wavelength is chosen for a specie whose concentration decays at an endpoint and a second wavelength is chosen for a specie whose concentration increases at the endpoint. Therefore, the ratio gives improved signal to noise.
However, as the IC device sizes have decreased, and the exposed open areas have correspondingly decreased, single and dual wavelength endpoint detection schemes have found limited use due to their reduced robustness for extracting a low signal-to-noise (S/N) endpoint signal from the process. Subsequently, process engineers have been presented with the formidable challenge of selecting the right wavelengths with sufficient robustness in a manufacturing environment and, as a result, more sophisticated endpoint detection schemes have arisen. The sophisticated endpoint detection schemes sample data at thousands of wavelengths (i.e. a broad emission spectrum is recorded at each instant in time during the etch process) and multivariate data analysis techniques such as Principal Component Analysis (PCA) are applied to extract the endpoint signal.
In PCA, several techniques, including eigenvalue analysis, singular value decomposition (SVD), and nonlinear partial least-squares (NiPALS) have been employed to identify the principal directions in the multi-dimensional space, where the variance in the data scatter is greatest. The dimension of the multi-dimensional space is equivalent to the number of variables recorded, i.e. the number of discrete wavelengths of the emission intensity are recorded. And therefore, PCA will identify the directions in the multi-dimensional space where the variations in the emission intensity are greatest. In other words, the principal component acts as a series of weighting coefficients for each variable. Typically, the first three or four principal components (corresponding to the three or four largest eigenvalues) are selected and employed for deriving the three or four endpoint signals from the newly recorded data. However, a shortcoming of the use of PCA for multivariate analysis of optical emission data includes the mathematical rigor and complexity such an analysis entails, and, more importantly, the lack of use of physical criteria associated with the etch process to extract a reduced set of data including the endpoint signal(s).
Therefore, what is needed is an improved apparatus and method for endpoint detection which overcomes the shortcomings identified above.